Backpropagation is a crucial algorithm used in neural network training. It involves calculating the error at the output layer and then propagating that error backward through the network to adjust the weights of the neurons in each layer. Afterward, an optimization algorithm, typically gradient descent, uses this error to update the parameters (weights and biases) of the network, enabling the model to learn and improve its performance.