Why does a simpler model performs better than a complicated one?
High variance means that your model's performance varies from data to data, which is bad for the model.
For example:
You used a polynomial classifier with high degree, so it will overfit your training data resulting in good accuracy, but when tried on a different dataset it will yield less accuracy , resulting in variance.


The image on the right is way too complex which results in overfitting.

The middle image shows a simpler model than the image on right, but fits the data better ie. generalize the data better