<h4 dir="auto">Describe the bug</h4>
<p dir="auto">When doing model selection on very high-dimensional data using <code class="notranslate">GridSearchCV</code>, I found that when my estimator design has a <code class="notranslate">ColumnTransformer</code> where one of the transformers has a columns spec that is a numpy array of e.g. 50k column names, that <code class="notranslate">GridSearchCV</code> and the embedded joblib <code class="notranslate">Parallel</code> calls consume a great deal of shared memory (/dev/shm) that's proportional to the number of <code class="notranslate">GridSearchCV</code> fits.  I always set <code class="notranslate">ColumnTransfomer</code> <code class="notranslate">n_jobs=1</code> in this setup.</p>
<p dir="auto">When troubleshooting, I found that for every fit <code class="notranslate">GridSearchCV</code> is doing, for some reason joblib <code class="notranslate">Parallel</code> is creating a duplicate pickle file of the same column name numpy array in /dev/shm.  I realized this by setting <code class="notranslate">JOBLIB_TEMP_FOLDER</code> and seeing all the duplicate pickle files and loading some of them to check.  The shared memory usage can add up to many GBs since for e.g. each pickle file might be 4 MB * 5000 fits = 20 GB.</p>
<p dir="auto">What I then found out which is stranger, is that if I instead make the <code class="notranslate">ColumnTransformer</code> columns spec a Python list instead of a numpy array then there isn't any joblib <code class="notranslate">Parallel</code> /dev/shm pickling issue but this causes major <code class="notranslate">GridSearchCV</code> and joblib <code class="notranslate">Parallel</code> performance degradation.  I see that each parallel job isn't even using close 100% CPU as if it's spending a lot of time with I/O or overhead.  This is not typical at all, in my experience model selection with most model designs in sklearn is very CPU bound with low overhead.  So changing the <code class="notranslate">ColumnTransformer</code> column spec dtype for a very large list of column names from a numpy array to Python list with everything else being the exactly same results in a substantial change in <code class="notranslate">GridSearchCV</code> behavior.</p>
<h4 dir="auto">Steps/Code to Reproduce</h4>
<p dir="auto">I need to still come up with a minimal example using <code class="notranslate">sklearn.datasets</code> or other public one, does anyone know of any very high-dimensional example dataset that can be imported for use with sklearn?</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="TODO"><pre class="notranslate"><code class="notranslate">TODO
</code></pre></div>
<h4 dir="auto">Expected Results</h4>

<h4 dir="auto">Actual Results</h4>

<h4 dir="auto">Versions</h4>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="System:
    python: 3.8.1 | packaged by conda-forge | (default, Jan 29 2020, 14:55:04)  [GCC 7.3.0]
executable: /home/hermidalc/soft/miniconda3/envs/sklearn-workflows/bin/python
   machine: Linux-5.5.8-200.fc31.x86_64-x86_64-with-glibc2.10

Python dependencies:
       pip: 20.0.2
setuptools: 45.2.0.post20200209
   sklearn: 0.22.1
     numpy: 1.18.1
     scipy: 1.4.1
    Cython: None
    pandas: 1.0.1
matplotlib: 3.1.3
    joblib: 0.14.1

Built with OpenMP: True"><pre class="notranslate"><code class="notranslate">System:
    python: 3.8.1 | packaged by conda-forge | (default, Jan 29 2020, 14:55:04)  [GCC 7.3.0]
executable: /home/hermidalc/soft/miniconda3/envs/sklearn-workflows/bin/python
   machine: Linux-5.5.8-200.fc31.x86_64-x86_64-with-glibc2.10

Python dependencies:
       pip: 20.0.2
setuptools: 45.2.0.post20200209
   sklearn: 0.22.1
     numpy: 1.18.1
     scipy: 1.4.1
    Cython: None
    pandas: 1.0.1
matplotlib: 3.1.3
    joblib: 0.14.1

Built with OpenMP: True
</code></pre></div>
<h4 dir="auto">Description</h4>
<p dir="auto">Vising the scikit-learn website using https is not possible and gives an error. Github pages now allows custom domains to use https so this error can be resolved. See <a href="https://blog.github.com/2018-05-01-github-pages-custom-domains-https/">https://blog.github.com/2018-05-01-github-pages-custom-domains-https/</a> on how to set this up.</p>
<h4 dir="auto">Steps/Code to Reproduce</h4>
<p dir="auto">Manually type in "<a href="https://scikit-learn.org" rel="nofollow">https://scikit-learn.org</a>" into a web browser.</p>
<h4 dir="auto">Expected Results</h4>
<p dir="auto">I should be able to visit the website using HTTPS without an error.</p>
<h4 dir="auto">Actual Results</h4>
<p dir="auto">Going to <a href="https://scikit-learn.org" rel="nofollow">https://scikit-learn.org</a> generates the following error page and does not direct me to the scikit learn website (this message's wording comes from Firefox specifically):</p>
<p dir="auto">"""<br>
Your connection is not secure</p>
<p dir="auto">The owner of scikit-learn.org has configured their website improperly. To protect your information from being stolen, Firefox has not connected to this website.</p>
<p dir="auto">scikit-learn.org uses an invalid security certificate. The certificate is only valid for the following names: *.github.com, github.com, *.github.io, github.io Error code: SSL_ERROR_BAD_CERT_DOMAIN<br>
"""</p>
<p dir="auto">A similar error is shown in Chromium.</p>
<h4 dir="auto">Versions</h4>
<p dir="auto">N/A</p>