A procedure in which the dataset is shuffled randomly and split into ‘k’ groups (or folds). The error is calculated ‘k’ times using a different fold for validation each time, and the average error is calculated (56).
Can be used for validating Machine Learning models.
How the k-fold cross-validation method works with an example of k=5 (57).