TypeError: 'KFold' object is not iterable

That depends on how you have imported the KFold.

If you have did this:

from sklearn.cross_validation import KFold

Then your code should work. Because it requires 3 params :- length of array, number of splits, and shuffle

But if you are doing this:

from sklearn.model_selection import KFold

then this will not work and you only need to pass the number of splits and shuffle. No need to pass the length of array along with making changes in enumerate().

By the way, the model_selection is the new module and recommended to use. Try using it like this:

fold = KFold(5,shuffle=False)

for train_index, test_index in fold.split(X):

    # Call the logistic regression model with a certain C parameter
    lr = LogisticRegression(C = c_param, penalty = 'l1')
    # Use the training data to fit the model. In this case, we use the portion of the fold to train the model
    lr.fit(x_train_data.iloc[train_index,:], y_train_data.iloc[train_index,:].values.ravel())

    # Predict values using the test indices in the training data
    y_pred_undersample = lr.predict(x_train_data.iloc[test_index,:].values)

    # Calculate the recall score and append it to a list for recall scores representing the current c_parameter
    recall_acc = recall_score(y_train_data.iloc[test_index,:].values,y_pred_undersample)
    recall_accs.append(recall_acc)

KFold is a splitter, so you have to give something to split.

example code:

X = np.array([1,1,1,1], [2,2,2,2], [3,3,3,3], [4,4,4,4]])
y = np.array([1, 2, 3, 4])
# Now you create your Kfolds by the way you just have to pass number of splits and if you want to shuffle.
fold = KFold(2,shuffle=False)
# For iterate over the folds just use split
for train_index, test_index in fold.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    # Follow fitting the classifier

If you want to get the index for the loop of train/test, just add enumerate

for i, train_index, test_index in enumerate(fold.split(X)):
    print('Iteration:', i)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

I hope this works