Fig. 2

Illustration of cross-validation. The data is randomly split into K evenly-sized blocks (in this Figure, K = 5), partitioning the data into a training set and a test set. For each fold, a model is fit on the training set and evaluated on the test set. The model that performs best (i.e. has the highest likelihood) on average over all K folds is the one that is selected