Here are a couple of options:

If the individual elements of c are transformable to an array, you can use the indices as the loop variable:

for k = 1:numel(c)
   disp(c{k}); 
end

first
second
third

This option is closest to what cellfun would do. (cellfun would be another option to replace the for loop entirely.)

If the elements are not transformable to an array (or you just want to do it a different way) you can use c as the loop index and grab the contents inside the loop:

for k = c
   disp(k{});
end

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This is the most direct translation of what you do in your example.

The documentation in for() states:

for index = values
   ...
end

where values has one of the following forms:

• ...

• valArray: creates a column vector index from subsequent columns of array valArray on each iteration. For example, on the first iteration, index = valArray(:,1). The loop executes for a maximum of n times, where n is the number of columns of valArray, given by numel(valArray, 1, :). The input valArray can be of any MATLAB data type, including a string, cell array, or struct.

Therefore, I assume there is a significant overhead and the compiler does not check whether 1:ksize == klist to exploit the faster implementation. In other words, per Eitan's comment, the JIT applies to the first two types of accepted values.

The whole problem is related to the following indexing task (column vs element):

tic
for m = 1:100000        
    for k = 1:ksize
        klist(:,k);
    end        
end
toc

tic
for m = 1:100000        
    for k = 1:ksize
        klist(k);
    end        
end
toc

Index column:  ~2.9 sec
Index element: ~0.28 sec

You can see how klist(:,k) effectively slows down the faster loop indicating that the issue in for k = klist is related to the column indexing used in this case.

For additional details see this lengthy discussion on (inefficient) indexing.