Backpropagation is an algorithm used to train artificial neural networks by adjusting the weights of connections based on the error or difference between predicted and actual outcomes. This process allows the network to learn from mistakes and improve its predictions over time. Backpropagation is a critical element in supervised learning and is widely used in training deep learning models.