In statistical learning, prediction tries to make accurate estimations for unseen data, without examining the specifics of the distribution. Contrast with inference, which makes broader conclusions about the data.
In general, more complex learning methods tend to increase predictability but reduce the interpretability. For instance, neural networks can predict fairly well, as do support vector machines. Least squares may be fairly limited by its linearity.