Attention mechanisms are a crucial component in neural networks that allow models to dynamically focus on different parts of the input data, enhancing performance in tasks like machine translation and image processing. By assigning varying levels of importance to different input elements, attention mechanisms enable models to handle long-range dependencies and improve interpretability.