Consider bias as error in Training Data and variance as error in Test Data.
Under fitting model:
High Bias and Low Variance [If you try to fit a simple model such that most of the training data points won’t be satisfied].
Over fitting model:
Low Bias and High Variance [If you try to fit a model such that most of the training data points would be exactly satisfied].
So, it is very important to build a Perfect Model such that it satisfies most of the training data points and gives better results for the test data [Low Bias and Low Variance].