Should I use cvpartition before using crossval function?

Hi friends,

I am trying to use k-fold cross validation in Matlab. I analyze the examples and Matlab documents about it but I am confused at one point. Can you tell me which usage is correct ?

Usage 1:

Model=fitcknn(feat,label,'NumNeighbors',k,'Distance','euclidean'); 
%feat is x data of all data set label is y data of all data set.
C=crossval(Model,'KFold',kfold);
ER=kfoldLoss(C);

Usage 2:

SVMModel = fitcecoc(trainingData,classes);
cp = cvpartition(classes, 'k', 10);
CVM = crossval(SVMModel, 'CVPartition', cp);
ER=kfoldLoss(CVM)

Also, does the usage of this line of code change according to the classifier used in the code? For example, if I use fitcsvm instead of fitcknn, should I remove any function? (I think, I should not.)

[Umit Kilic]

Cross-validation partition, specified as the comma-separated pair consisting of 'CVPartition' and a cvpartition object created by the cvpartition function. crossval splits the data into subsets with cvpartition.

I see the above information from crossval documentation. I think first usage is enough, because the info says that crossval uses cvpartitionn function already. Even so, I would like to hear your response.