Semi-supervised learning is an approach that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlike supervised learning, which requires a lot of labeled data, semi-supervised methods aim to leverage the readily available unlabeled data to improve learning accuracy and efficiency. This method is often used in situations where labeling data is expensive or time-consuming, such as in natural language processing or computer vision tasks.