The Rectified Linear Unit, or ReLU, is a popular activation function used in neural networks. It transforms input values by outputting them directly if they are positive, and zero if they are negative. This simple mathematical operation helps introduce non-linearity into the model, allowing it to learn complex patterns in data. ReLU is favored for its computational efficiency and effectiveness in training deep learning models. Unlike other activation functions, it mitigates the vanishing gradient problem, which can hinder learning in deeper networks. However, it can suffer from the "dying ReLU" issue, where neurons become inactive and stop learning altogether.