The Rectified Linear Unit (ReLU) activation function is widely used in deep learning. Its primary purpose is to introduce non-linearity into the network, allowing neural networks to learn and model complex relationships in the data. ReLU sets negative inputs to zero and keeps positive inputs unchanged. This simple thresholding operation allows ReLU to preserve positive values and discard negative values, effectively activating or deactivating neurons based on the input.