Semi-supervised learning is a machine learning approach that leverages both labeled and unlabeled data for training, aiming to improve learning accuracy compared to using only labeled data. It is particularly useful when acquiring a fully labeled dataset is expensive or time-consuming, allowing models to learn from a small amount of labeled data supplemented by a larger pool of unlabeled data.