Self-Attention is a mechanism within neural networks that allows the model to focus on different parts of the input sequence when processing data. By assigning varying attention scores to different elements of the sequence, self-attention enhances the model's ability to capture relationships and dependencies, improving its performance on tasks like text generation and translation.