Self-training is a semi-supervised learning technique where a model is initially trained on a small labeled dataset and then iteratively retrains itself using its own predictions on a larger unlabeled dataset. This method leverages the model's ability to generalize from limited labeled data to improve its performance and expand its training dataset autonomously.