I am having a lot of confusion between GridSearchCV and K fold Cross Validation. I know that GridSearch is only for hyperparameter optimization and K Fold will split my data into K folds and iterate over them (cv value). So should I first split my data into train and validation sets, apply GridSearch on training data for "best parameters" and then use K Fold on my training data using the "best parameters" I got from GridSearch and then finally train model on whole data(train + validation set)? Or is there some other order of doing the above?
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EDIT: For doing k-fold cross-validation, you don't need to split the data into training and validation set, it is done by splitting the training data into k-folds, each one of which will be used as a validation set in training the other (k-1) folds together as training set.
Idarmis Knight Soto ok so if I want to perform gridsearchcv, should I do it after k fold cv or before?
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