Backpropagation is a crucial algorithm used in training artificial neural networks. It involves computing the gradient of the loss function with respect to each weight in the network by propagating the error backward through the layers. This process allows the network to adjust its weights and minimize the error, enabling the model to learn and improve its performance over time.