@AN6US, can you eloborate your point about "orthogonal vector space"?

I appreciate your answer. Still we can select a model from hyperparamter tuning, but in case of CV we dont get a fitted model and only get scores?

Okay thanks, I am accepting the answer as "which accuracy is to be used" makes sense. But is it possible for you to elaborate more on "validation set is used to build/select a better model (e.g. avoid over-fitting) vs in your case, 10-fold CV tests an already-built model" for me and future readers?

@Esmailian train_score is also an average of 10 scores? Also, to do a similar kind of thing with GridSearchCV(in case hyper paramter tuning and cross-validation are required in one step) can we use return_train_score=true? is it same?