Comparing model evaluations of machine learning and statistics I'm a machine learning practitioner who spends most of my time doing applications via Python in machine vision with neural networks and remote sensing data, and evaluating model performance mostly using cross-validation based techniques etc.
Statistics has been (of course) part of my learning path and I am familiar with the basic concepts of statistics, but I am not actively doing the work of a "pure" statistician, that is, using e.g. R/SAS-software to fit generalized linear models, calculating the $t/p$-values, ANOVA and testing statistical significance of model covariates.
Even though I've been exposed to these fields for many years now, it still is not clear to me  how (traditional/pure) statistics and machine learning differ from each other. I know that the general concensus on the difference is that statistics cares more about explaining the data, whereas machine learning (ML) is interested in making predictions, even though the differences are sometimes vague and both fields use the same methods.
Now in ML, usually when we have fitted some model into the data, we care about how well the model has learned the underlying pattern from the data, and we measure this learning by using an independent test data set. We never want to have a perfect fit of the model into the training data, because this would lead into overfitting and poor generalization capability. So the relevant question in ML (to my understanding) is never about "how good the fit of the model is to the data?", but rather "how well the model is able to predict new situations, that is, generalize".
When I listen to my statistician colleagues, I notice them talking mostly about the $t/p$-values, the $R^2$-values, $F$-statistics, differences in the means of two groups in ANOVA etc.
To me, from a ML perspective, my statistician colleagues seem to be concerned exactly about the "goodness-of-fit" of their models/covariates into the data and there is no independent test data set, which they use to validate their models. Well, of course, they don't do this, because this is not the goal, but rather to explain the data with the explicit assumptions we have made on the data (normality etc. the usual ones).
Now, I most probably am wrong due to my lack of knowledge on the subject, but it somehow seems odd to me why"pure" statisticians are so interested in these "goodness-of-fit"-statistics, because don't the $p$-values, $R^2$ etc. essentially in the end measure exactly this, that is the model fit (given that our distribution assumptions are correct)? For example, we all know that neural networks are universal approximators, meaning we can fit them with 100% accuracy into any (continuous) function we arbitrarily choose by just adding enough neurons into the model. Now, wouldn't this universal approximating neural network tuned into our data like hell have a huge statistical significance in the $p$-values or $R^2$-metrics, if we look at the model fit from a "pure" statistician's perspective? As a summary, would a statistician (does he?) conclude that: "we have found something truly significant now" in this neural network scenario? A ML scientist would produce an independent test sample, feed it to the network, and conclude that the model is overfitting the training data as hell and no pattern has been found. In other words, ML scientist would conclude "nothing significant has been found".
Maybe I asked my question too vaguely, but here it is summarized: Is it true to some extent that statisticians are usually more concerned about the model's goodness-of-fit and the corresponding metrics of significance, and not that much about model's generalization capability,  and vice versa for the ML scientists?
Thank you in advance for any answers, which help me clear up my confusion :)


Is it true to some extent that statisticians are usually more
concerned about the model's goodness-of-fit and the corresponding
metrics of significance, and not that much about model's
generalization capability, and vice versa for the ML scientists?

No. Measuring generalisation capability is a large portion of statistics practice. Cross validation and bootstrapping techniques addresses the question of how well statistical models generalise and selecting a model in Occam's sense: A survey of cross-validation procedures for model selection.
One thing seems to diverge significantly with modern Machine Learning (ML) community, unfortunately, that ML community stop practicing Occam's razor. This is partially because deep learning defy the core paradigms, such as phenomenon of double deep descent. ML community try to establish generalisation via test-train curves on detecting overfitting, i.e., single holdout only. Actually in overfitting, we require two models to compare, not only a holdout on a single model. This is best described by Andrew Gelman, see What is overfitting:

Overfitting is when you have a complicated model that gives worse predictions, on average, than a simpler model.

However, there is now a significant research activity in deep learning as well to introduce Occam's razor indirectly via Neural Architecture Search (NAS). It doesn't aim at directly overfitting rather a model compression, but it is actually form of prevention of overfitting in Gelman's definition.
