A special case of k-fold cross-validation is the Leave-one-out cross-validation (LOOCV) method. In LOOCV, we set the number of folds equal to the number of training samples (k = n) so that only one training sample is used for testing during each iteration, which is a recommended approach for working with very small datasets. A slight improvement over the standard k-fold cross-validation approach is stratified k-fold cross-validation, which can yield better bias and variance estimates, especially in cases of unequal class proportions,
