Cross-validation is performed by training the model multiple times on the entire dataset to ensure maximum learning.

Cross-validation involves splitting the data into training and validation sets once, then training the model on the training set and validating on the validation set.

Cross-validation in a pipeline ensures that the model is trained and validated on different subsets of the data, providing a more robust estimate of model performance and helping to prevent overfitting.

Cross-validation is only applicable to classification problems and not to regression problems.