Probably Approximately Correct (PAC) Learning is a framework in computational learning theory that quantifies the ability of a learning algorithm to generalize from a limited set of examples to unseen data. It provides theoretical guarantees on the performance of the learning algorithm, specifically concerning the probability that the learned hypothesis is approximately correct within an acceptable error margin given enough training samples.