10-fold cross validation with cross val or without

Dear, I'm trying to perform 10-fold HOLDOUT cross validation to train a knn classifier.

For each HOLDOUT I also want to use an internal HOLDOUT to oprtime the knn hyperparameters through Bayesian otpimization (which evaluated the accuracy on the internal holdouts)

Dear, I'm trying to perform 10-fold HOLDOUT cross validation to train a knn classifier.

For each HOLDOUT I also want to use an internal HOLDOUT to oprtime the knn hyperparameters through Bayesian otpimization (which evaluated the accuracy on the internal holdouts).

Therefore, I would like to use a procedure like this one:

nfoldExtHoldout = 10;

dataMat = randi(100, 5001,11); %trainig set composed of 5001 points, each being an 11 dimensional (11 features) point.
labels = rand(5001,1)>0.7;     % unbalanced labels

numPts = size(dataMat,1);

for nF = 1: nfoldExtHoldout


    [trainIdx,testIdx] = crossvalind('Holdout',numPts,0.3);
	trainSet =   dataMat(trainIdx,:);      %I randomly take the 70 percent of samples in the training set
	labelsTrain = labels(trainIdx);					% corresponding labels for training points
	
	testSet =   dataMat(testIdx,:);      %the remaining 30 percent of samples in the test set
	labelsTest = labels(testIdx);					% corresponding labels for test points

	cv = cvPartition(numPts, 'Holdout', 0.3);

	% train a knn classifier through Bayesian optimization to choose the best knn parameters
	mdlknn = fitcknn(trainSet, labelsTrain,  'OptimizeHyperparameters',  'all', ...
                    'HyperparameterOptimizationOptions', ...
                     struct('Verbose', 0,'UseParallel',true, 'CVPartition', cv));

        
        % predict test data
    testPred = predict(mdlknn, testSet);
	conf = confusionmat(testPred, labelsTest);
	knnLoss(nF) = 1- sum(diag(conf))/numPts;

end

is this correct??

What's the difference between the code above and this one?

nfoldExtHoldout = 10;

dataMat = randi(100, 5001,11); %trainig set composed of 5001 points, each being an 11 dimensional (11 features) point.
labels = rand(5001,1)>0.7;     % unbalanced labels

numPts = size(dataMat,1);


	cv = cvPartition(numPts, 'Holdout', 0.3);

	% train a knn classifier through Bayesian optimization to choose the best knn parameters
	mdlknn = fitcknn(dataMat, labels,  'OptimizeHyperparameters',  'all', ...
                    'HyperparameterOptimizationOptions', ...
                     struct('Verbose', 0,'UseParallel',true, 'CVPartition', cv));

        for nf = 1: nfoldExtHoldout
            crossValKnn = crossval(mdlknn,'Holdout', rHoldout);
            knnLoss(nf) = kfoldLoss(crossValKnn);
        end

In other words, I dont' understand what is crossval doing.

I suppose in the code below, the hyperparameter optimization is run just once and the optimized hyperparameters are the same for all the external fold right?

Then crossval trains a separate knn for each holdout???