Leave-one-out cross validation. LOOCV uses a single observation
from the original sample as the validation data, and the remaining
observations as the training data. This is repeated such that each
observation in the sample is used once as the validation data. This is
the same as a K-fold cross-validation with K being equal to the number of
observations in the original sample. Leave-one-out cross-validation is
usually very expensive from a computational point of view because of the
large number of times the training process is repeated.