The correct answer is C. Both A and B.
Cross-validation is a resampling procedure for evaluating the performance of a model on unseen data. It is used to avoid overfitting, which is a common problem in machine learning. Overfitting occurs when a model learns the training data too well and does not generalize well to new data.
There are several different ways to perform cross-validation, but the most common is k-fold cross-validation. In k-fold cross-validation, the data is divided into k equal-sized subsets. The model is then trained on k-1 of the subsets and evaluated on the remaining subset. This process is repeated k times, and the average performance of the model is reported.
Cross-validation is a powerful tool for evaluating the performance of a model. It is often used in conjunction with other techniques, such as hyperparameter tuning, to improve the performance of a model.
Option A is correct because cross-validation can be used to assess the predictive performance of a model. By evaluating the model on unseen data, we can get an idea of how well the model will generalize to new data.
Option B is also correct because cross-validation can be used to judge how the trained model performs outside the sample on test data. By dividing the data into training and test sets, we can ensure that the model is not overfitting the training data.
Option C is the correct answer because it includes both Option A and Option B.