It usually refers to a plot of the prediction accuracy/error vs. the training set size (i.e: how better does the model get at predicting the target as you the increase number of instances used to train it)
Usually both the training and test/validation performance are plotted together so we can diagnose the bias-variance tradeoff (i.e determine if we benefit from adding more training data, and assess the model complexity by controlling regularization or number of features).
