I understand it like this, the plots are about a hypothetical model with two features represented as x and y axes and two classes represented as circles and crosses. The drawed blue line represents the model that is trying to separate those classes.

At the left you have a model that is very simple and the expected value of its predictions is not very close to reality (high bias). At the right you have a model that is very complex and adjust very well to the existing data, but has much variability in its predictions so does not generalize well (high variance).

If you want to know more about this, @nramon has posted a very good explanation here: