The self-attention mechanism, crucial in transformer models, allows each token in a sequence to dynamically focus on different parts of the input sequence, capturing dependencies regardless of their distance. This mechanism enhances parallelization and scalability, leading to more efficient and powerful language understanding and generation tasks.