Self-supervised learning is a technique where models learn to predict part of the input from other parts, effectively generating labels from the data itself. Unlike traditional supervised learning which requires labeled datasets, self-supervised learning leverages the natural structure of the data to create auxiliary tasks, such as predicting the next frame in a video. It bridges the gap between supervised and unsupervised learning by reducing the need for manually labeled data.