title: how to select model if there are multiple models with same or close validation errors
body: <p>Questions about model selection using cross-validation</p>
  
  <p>Let's say a dataset was split as training and testing sets. Multiple models were compared using cross-validation on training datasets. </p>
  
  <p>In one scenario, some of models yielded exact same validation errors. </p>
  
  <p>In another scenario, the models accuracy might rank as 99%, 98%, 97%, 95%,90%.......etc. </p>
  
  <p>For these two scenarios, could you please advise how and why to choose a model?</p>
  
  <p>I understand test dataset is just designed to evaluate generalization error. But for the scenarios above, whether it is time to use test dataset to evaluate those models. </p>