What is a primary concern when dealing with a high variance in machine learning models?

In machine learning, high variance refers to a model's sensitivity to the fluctuations in the training data. When a model exhibits high variance, it tends to capture noise along with the underlying patterns, leading to a situation known as overfitting. This occurs when the model learns the training data too well, including its random noise, rather than generalizing from it.

When overfitting happens, the model performs exceptionally well on the training data but fails to predict or perform accurately on unseen data because it has essentially memorized the training examples rather than learned from them. This makes overfitting a primary concern, as the goal of a model is to generalize well to new, unseen data.

In contrast, the other possible concerns mentioned do not adequately relate to high variance. The risk of underfitting generally arises in low variance scenarios where the model is too simplistic and fails to capture the complexity of the data. Increased model complexity does not inherently mean high variance; a complex model can still generalize well if it is effectively regularized. Reduced model accuracy can be a byproduct of various issues, including both overfitting and underfitting, but is not a primary concern specifically associated with high variance itself.