Backpropagation is a crucial method for training neural networks, allowing them to improve accuracy by adjusting weights through gradients calculated with respect to a loss function. This process involves two main phases: a forward pass, where inputs traverse through the network, and a backward pass, where errors are propagated back to update weights. It's similar to learning from mistakes; when a network guesses wrong, backpropagation helps correct it, enhancing future predictions.