The bias-variance trade-off is a key problem in your model search. While bias represents how well your model can capture the salient details of a problem, and generally correlates with more complex algorithms, it comes at the trade off of variance. Variance is the degree to which on individual predictions your estimators stray from the mean output on those values.  High variance means that a model has overfit, and incorrectly or incompletely learned the problem from the training set. Most commonly, high bias = underfitting, high variance = overfitting.
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For more academic sources, consider reading the slides from this fantastic Carnegie Mellon lecture.

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