Pseudo-labeling is a semi-supervised learning technique where the model uses its own predictions to label unlabeled data, effectively creating additional labeled data to improve training. This approach leverages the model's existing knowledge to iteratively refine its performance on a larger dataset, often leading to enhanced accuracy and generalization.