(Enter the username of the target account you wish to use.)
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Split your training data for both models.So how should one go about conducting a fair comparison? Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context. So your score for the grid search is going to be worse than your baseline.
Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold). It's working with less data since you have split the X_train sample. The grid searched model is at a disadvantage because: This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen. 
Then you're using the fitted model to score the X_train sample. Your baseline model used X_train to fit the model.
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