Semi-supervised learning is a machine learning approach that combines a small amount of labeled data with a large amount of unlabeled data. This method is useful when acquiring labeled data is expensive or time-consuming, allowing models to learn from both types of data to improve their performance. In this technique, algorithms leverage the structure of the unlabeled data to better understand the relationships and patterns within the dataset. By doing so, they can make more accurate predictions, even with limited labeled examples, making semi-supervised learning a valuable tool in fields like natural language processing and computer vision.