Backpropagation is the primary algorithm used for training neural networks. It calculates the gradient of the loss function with respect to each model parameter and propagates these gradients backward through the network layers. This process updates the weights using optimization techniques like gradient descent, allowing the network to minimize error over successive iterations. Backpropagation is fundamental to deep learning, enabling complex architectures such as convolutional and recurrent neural networks to learn from large datasets.