Why Cross-Validation score is less than the Test Score? I have a data set 1035 x 16, implemented random forest regressor algorithm after all the possible feature processing techniques, test/ train split is 20/80, for cross validation I used 5 K-Folds and  following are the results:
Test Score : 72.82762577833321
Training Score: 97.38484784016136
Validation score : [0.63171505, 0.64145713, 0.67557705, 0.66368883, 0.6085836 ]
Validation score avg : 64.42043307160208

I know model is over-fitting but how I can interpret,  the Cross Validation score is less than Test score . 
when the Cross Validation score is less than Test score ?
when the Cross Validation score is greater than Test score ?
which is  better ?
 A: The same condition happened to me many times. I believe it is normal to get such results, as cross validation score is basically a "test score". It is also the average of the k-folds so it is a more stable score than just the final test score, which is a single score. So, for me it is okay to get such result. But I fear it is a little an opinion, rather than a concrete answer.
A: A high training score and low validation score signals overfitting as you’ve also mentioned. And, overfitting means your test results is subject to high variance, which naturally explains the large difference between the test and validation sets.
A: Training score is more than the validation score when the model overfits. Typically, the validation score is less than the training score, because model fits on training data, and validation data is unseen by the model. 
Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
But sometimes validation score can be more, it just means your validation data is easier to classify. In this case, try to take a bigger validation dataset, or use multiple fold cross-validation and this case should disappear. 
I hope this helps.
