In statistical learning, a consistent predictor is an algorithm or model that, under mild conditions, will eventually converge to the optimal predictor as the size of the training data increases to infinity. This theoretical guarantee means that the algorithm, such as the $1$-nearest neighbor method, will ultimately find the best possible mapping from inputs to outputs when provided with sufficient data.