LOOCV or Leave-One-Out Cross Validation. LOOCV uses one 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 a large number of times the training process is repeated.

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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.

Recommended reading list:

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