Cross-validation: error estimation and bias When obtaining the error estimation of a model over a dataset using k-fold cross-validation, lower values of the error estimation necessarily imply a lower bias? Are both concepts, error estimation and bias, equivalent?

Higher test errors can also indicate lower bias, e.g. if the training error is low. So, lower error is not equivalent to lower bias.
