This next section of the lesson should look very familiar. You'll follow the same four steps to cross validate and score your classification models as you did with your regression algorithm. First, create untrained models for each model class. Then train each model and tune its hyperparameters. Third, use the train models to make predictions based on the inputs from your testing data. And finally, compare the predictions against the actual target values from your testing data to score each model. Start by importing the gridsearchcv function into your Jupyter Notebook. And remember, use the format that you see here to create each untrained model. You're going to call the gridsearchcv function. Your first argument is going to be the pipeline associated with that model. Then the hyperparameter grid associated with that model. And finally, the number of folds you want to use for your cross validation.