The following methods can be used to overcome high variance:
Reducing the number of features in the model.
Replacing the current model with a simpler one.
Increasing the training data diversity to balance out the complexity of the model and the data structure.
Avoiding high-variance algorithms (support vector machines, decision trees, k-nearest neighbors, etc.) and opt for low-variance ones such as linear regression, logistic regression, and linear discriminant analysis.
Performing hyperparameter tuning to avoid overfitting.
Increasing regularization on inputs to decrease the complexity of the model and prevent overfitting.
Using a new model architecture. (Like with the high bias, this should be considered a last resort if other methods are not effective.)