The Tanh function, or hyperbolic tangent function, is a widely used activation function in neural networks, known for its ability to map input values to an output range of -1 to 1, which helps in centering the data and mitigating issues like vanishing gradients. It is particularly useful in hidden layers of neural networks as it provides a smoother gradient and more robust learning compared to the sigmoid function.