Gradient Descent Optimization is an iterative algorithm used to minimize a function by moving towards the steepest descent as defined by the negative of the gradient. It is crucial for training machine learning models, particularly neural networks, as it efficiently finds the optimal parameters by updating them iteratively based on the learning rate and the gradient of the loss function.