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 leverages the strengths of both supervised and unsupervised learning, allowing models to learn from the labeled examples while also discovering patterns in the unlabeled data. This technique is particularly useful in situations where obtaining labeled data is expensive or time-consuming. By using algorithms that can effectively utilize both types of data, semi-supervised learning can improve model accuracy and performance, making it a valuable tool in fields like natural language processing and computer vision.