In model development, parameters are the core components that a model learns from data during training. They define the functions that map input data to outputs. For instance, in a neural network, weights and biases are parameters that adjust during training to minimize error on tasks such as classification. Parameters differentiates from hyperparameters, which are external configurations set prior to training, like learning rate or epochs.