Which of the following is correct use of cross validation?

The correct answer is D. All of the mentioned.

Cross-validation is a resampling procedure for evaluating the performance of a statistical model on unseen data. It is used to estimate the error rate of a model and to select the best model among a set of candidate models.

Cross-validation can be used for the following purposes:

• Selecting variables to include in a model: Cross-validation can be used to select the best subset of variables to include in a model. This is done by splitting the data into a number of folds, and then training the model on each fold and evaluating it on the remaining folds. The variables that are most important for the model are those that are selected most often across the folds.
• Comparing predictors: Cross-validation can be used to compare the performance of different predictors. This is done by splitting the data into a number of folds, and then training the model on each fold and evaluating it on the remaining folds. The predictor that performs best on average across the folds is the best predictor.
• Selecting parameters in prediction function: Cross-validation can be used to select the best parameters in a prediction function. This is done by splitting the data into a number of folds, and then training the model on each fold and evaluating it on the remaining folds. The parameters that produce the best results on average across the folds are the best parameters.

Cross-validation is a powerful tool that can be used to improve the performance of statistical models. It is a valuable tool for any data scientist or statistician.