The delta parameter in Huber loss determines the transition point between the quadratic and linear regions of the loss function. A smaller delta value makes the loss function more sensitive to small errors, while a larger delta value makes it more robust to outliers. Choosing the optimal delta value depends on the specific problem and the distribution of errors in the data. One common approach is to use cross-validation, where you train models with different delta values and select the one that performs best on a validation set.