The advantage of k-fold CV is that it often gives more accurate estimates of the test error rate than does LOOCV. This has to do with a bias-variance trade-off. The validation set approach can lead to overestimates of the test error rate, since in this approach the training set used to fit the statistical learning method contains only half the observations of the entire data set ( it means the model does not have enough information about the data set.) LOOCV will give approximately unbiased estimates of the test error since each training set contains n − 1 observation, which is almost as many as the number of observations in the full data set. K-fold CV for, say, k = 5 or k = 10 will lead to an intermediate level of bias, since each training set contains (k − 1)n/k observations—fewer than in the LOOCV approach, but substantially more than in the validation set approach.

Note: LOOCV has a higher variance than does K-fold CV with k < n.