Difference between GridSearchCV and Cross_Val_Score

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I have a binary time series classification problem.

Since it is a time series, I can't just train_test_split my data. So, I used the object tscv = TimeSeriesSplit() from this link, and got something like this:

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I can see from GridSearchCV and cross_val_score that i can pass as parameter my split strategy cv = tscv. But my question is, whats the difference between GridSearchCV and coss_val_score? Using one of them is enough to train/test my model? or should i use both? First the GridSearchCV to get the best hyperparamaters and then the cross_val_score?

cross-validation gridsearchcv scikit-learn python
1 Answers

Grid search is a method to evaluate models by using different hyperparameter settings (the values of which you define in advance). Your GridSearch can use cross validation (hence, GridSearchCV exists) in order to deliver a final score for the the different parameter settings of your model. After the training and the evaluation (after the grid search has finished), you can take a look at the parameters with which your model performed best (by taking a look at the attribute best_params_dict).So, Grid search is basically a brute forcing strategy in which you run the model with all possible hyperparameter combinations. With coss_val_score you don't perform the grid search (you don't use the strategy mentioned above with all predefined params), but you get the score after the cross-validation. I hope it is now clear.

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