Token masking is a technique used in natural language processing models, particularly transformers, to hide certain parts of the input data during training to encourage the model to learn contextual relationships. It is crucial for tasks like masked language modeling, where the model predicts missing tokens based on surrounding context, enhancing its understanding of language structure and semantics.