The Rectified Linear Unit (ReLU) is a widely used activation function in neural networks, particularly in deep learning models. It transforms the input by outputting the input directly if it is positive, and zero otherwise, mathematically expressed as ReLU(x) = max(0, x). This simple yet effective function introduces non-linearity into the model, allowing it to learn complex patterns. ReLU is favored for its computational efficiency and ability to mitigate the vanishing gradient problem, which can hinder the training of deep networks. Variants like Leaky ReLU and Exponential Linear Unit (ELU) have been developed to address some of its limitations.