In machine learning, k-fold cross validation is a type of cross validation in which the dataset is split into k subsets and the validation process is repeated k times. Each time k-1 subsets are used for training the ML model, while one (1) subset is used for testing/validation. The final validation generalization capability/performance of the ML model is calculated by averaging the performances of all iterations.
