Dot-Product Attention is a mechanism in neural networks that calculates the relevance of inputs by computing the dot product between query and key vectors, which is then scaled and normalized to focus on the most pertinent parts of the input data. This approach is fundamental to the functioning of transformer models, enabling them to capture dependencies and relationships across different parts of the input sequence efficiently.