What best describes a statistical learning method if results are similar across multiple training datasets?

A statistical learning method that yields similar results across multiple training datasets indicates that it demonstrates low variance. Low variance means that the model's predictions do not fluctuate significantly with changes in the data used for training. This stability suggests that the model is capturing the underlying patterns in the data effectively rather than being overly sensitive to slight variations in the input data.

In statistical modeling, high variance typically arises when a model is too complex and fits the training data very closely, which can lead to overfitting. Conversely, a model exhibiting low variance is likely generalizing well to unseen data, making it more reliable for predictions in practical applications. Thus, the observation of consistent results across different training datasets confirms the model's robustness and generalization capability, supporting the assertion that the method has low variance.