I was wondering if my methodology makes sense. I am using GridSearchCV with cross-validation to train and tune model hyperparameters for a bunch of different model types (e.g. Regression Trees, Ridge, Elastic net, etc.). Before fitting the models I leave out 10% of the sample for model validation using train_test_split. (see Screenshot). I select the models with the best parameters to make predictions on the unseen validation set.
Am I missing something, as I haven't seen someone doing this when evaluating model accuracy while tuning for model parameters?