Mini-batch Gradient Descent is an optimization algorithm that combines the efficiency of batch gradient descent with the robustness of stochastic gradient descent by updating model parameters using a subset of the training data. It strikes a balance between convergence speed and computational efficiency, making it suitable for training large-scale machine learning models.