Xavier Initialization is a method used to set initial weights in neural networks to ensure that the variance of the outputs of each layer is the same as the variance of its inputs, which helps in preventing vanishing or exploding gradients during training. It is particularly useful for activation functions like sigmoid and hyperbolic tangent, as it maintains a balance that facilitates efficient training and convergence.