Leave-One-Out Cross-Validation (LOOCV) is a validation technique where a single observation from the dataset is used as the validation set, and the remaining observations are used as the training set, iterating over all observations. This method is exhaustive and can provide a nearly unbiased estimate of the model's generalization ability, but it is computationally expensive for large datasets.