Out-of-Bag Error is a method used in random forests to estimate the prediction error of the model without the need for a separate validation set. It leverages the bootstrap aggregating (bagging) process where each tree is trained on a different subset of the data and the performance is evaluated on the samples not included in that subset, known as out-of-bag samples.