In machine learning, Variance refers to the error coming from the model being too complex and leading to overfitting.
A model with high variance has a prediction error that changes a lot depending on the training input because it fits the training set very closely; its performance drop dramatically on the test set.
Together with bias, this is a fundamental concept to understand the bias-variance trade-off.
It must not be confused with the statistical concept of variance.