Cross validate after tuning hyperparameter I want to build classification machine learning using logistic regression. First i check my model roc_auc score using train test split and check with k cross validation. Turns out there is indication that my model is overfit.. so i decided to do hyperparameter tuning.
After i got some best parameter from hyperparameter tuning using grid search cv. Should i check my model roc_auc score once more with k cross validation?

You already used cross-validation during the grid search, so it won't make much sense to run cross-validation again. What would make sense is to split the data into train and test samples before tuning the parameters, then use cross-validation on the train set, and after finding the hyperparameters, test the model on the held-out test set.
