The learning curve is a plot between the training data used against the training and test error, plotted to diagnose the learning algorithm in order to minimize the reducible errors. The following example is a typical case of high variance:

The following diagram is a typical case of high bias. The training error and test error are too close and thus the model has under-fit. We need to choose a more complex algorithm which can fit well on this data and provide us with better generalization ability.