Hi @archu92, it is normal to have such doubt. It is not suggested to use error estimation from one single fold. “What a coincidence! 100 accuracy ” Usually we take average of MSE(mean squared error) or average of accuracy from 10 cross validated models. That is exactly what you will see in the results view for 'Performance' output of a cross-validation operator.
For example, you can have ouput for different performance criterion in the performance vector view,
accuracy: 66.9048% +/- 7.2695% (mikro: 66.8269%) shows average accuracy with its standard deviation
AUC (optimistic): 0.810101 +/- 0.078353 (mikro: 0.810101) (positive class: Mine) shows average Aera Under Roc Curve with its standard deviation