Answer
A model with high bias makes strong assumptions and underfits the data, while a model with high variance follows the training data very closely and overfits. The goal is to find a balance where both errors are reasonably low.
Real-world example A simple straight line model predicting house prices may miss important patterns, high bias, while a very complex model may fit every small detail of the training houses and fail on new houses, high variance.