How to loop through multiple lists/dict?

I have the following code which finds the best value of k parameter in the KNNImputer. Basically it is looping through the list of k_value and for each element, it is fitting the KNNImputer to the model and in the end appending the result to an empty dataframe.

lire_model = LinearRegression()
k_value = [1,3,5,7,9,11, 13, 15, 17, 19, 21]
k_value_results = pd.DataFrame(columns = ['k', 'mse', 'rmse', 'mae', 'r2'])

scoring_list = ['neg_mean_squared_error', 'neg_root_mean_squared_error', 'neg_mean_absolute_error', 'r2']

for s in k_value:
    imputer = KNNImputer(n_neighbors = s)   
    
    imputer.fit(train_x1_num)
    train_x2 = pd.DataFrame(imputer.transform(train_x1_num), columns = train_x1_num.columns)
    test_x2 = pd.DataFrame(imputer.transform(test_x1_num), columns = test_x1_num.columns)
    
    enc = ce.CatBoostEncoder()
    enc.fit(train_x3, train_y)
    train_x4 = pd.DataFrame(enc.transform(train_x3), columns = train_x3.columns)
    test_x4 = pd.DataFrame(enc.transform(test_x3), columns = test_x3.columns)

    base_score = cross_validate(lire_model, train_x4, train_y, cv = 5, scoring = scoring_list, 
                                n_jobs = -1)
    
    row = {
            'k': s,
            'mse' : -1 * base_score['test_neg_mean_squared_error'].mean(),
            'rmse' : -1 * base_score['test_neg_root_mean_squared_error'].mean(), 
            'mae' : -1 * base_score['test_neg_mean_absolute_error'].mean(), 
            'r2' : base_score['test_r2'].mean()
          }
    
    k_value_results = k_value_results.append(row, ignore_index = True)

If I have more than 1 list through which I want to loop through and perform the same functionality as above code, how can I do that?

For example:-

list1 = [a, b, c, d]
list2 = [e, f, g]

I want to loop through both the lists and for each combination of parameters (total 4*3 =12 combinations) I want the results. Basically I want to GridSearch over multiple lists without using sklearns GridSearchCV function.

Any ideas?